A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.
Department of Mathematics, University of California, Davis, CA.
Department of Cellular Biology, University of Georgia, Athens, GA.
- Published Article
Conference record. Asilomar Conference on Signals, Systems & Computers
- Publication Date
Nov 01, 2016
Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C(τ), as opposed to standard methods that decompose the time series, X(t), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.
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This record was last updated on 10/20/2019 and may not reflect the most current and accurate biomedical/scientific data available from NLM.
The corresponding record at NLM can be accessed at https://www.ncbi.nlm.nih.gov/pubmed/28649174